Assolutamente possibile e molto semplice.

Usa p2j per convertire il codice sorgente Python in Jupyter Notebook.

Dalla riga di comando, esegui
--> pip install p2j

quindi vai alla directory in cui si trova il tuo file. -->( per esempio-> cd download , se il file è nella directory di download)

quindi esegui questa riga

-->p2j iltuofile.py

Questo creerà un file iltuofile.ipynb


FACCI SAPERE SE FUNZIONA 😊👨‍💻

Infatti...

Allora ecco il codice completo per colab :

!pip install basemap

e poi

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import datetime
import time

from mpl_toolkits.basemap import Basemap

from keras.models import Sequential
from keras.layers import Dense

from keras.wrappers.scikit_learn import KerasClassifier
import tensorflow as tf
from sklearn.model_selection import GridSearchCV

#read data
data = pd.read_csv("/content/Dataset_Terremoti.csv")
data = data[['Date', 'Time', 'Latitude', 'Longitude', 'Depth', 'Magnitude']]
data.head()

#preprocessing dati
timestamp = []
for d, t in zip(data['Date'], data['Time']):
    try:
        ts = datetime.datetime.strptime(d+' '+t, '%m/%d/%Y %H:%M:%S')
        timestamp.append(time.mktime(ts.timetuple()))
    except ValueError:
        # print('ValueError')
        timestamp.append('ValueError')
timeStamp = pd.Series(timestamp)
data['Timestamp'] = timeStamp.values
final_data = data.drop(['Date', 'Time'], axis=1)
final_data = final_data[final_data.Timestamp != 'ValueError']
final_data.head()


#display data
m = Basemap(projection='mill',llcrnrlat=-80,urcrnrlat=80, llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')

longitudes = data["Longitude"].tolist()
latitudes = data["Latitude"].tolist()
#m = Basemap(width=12000000,height=9000000,projection='lcc',
            #resolution=None,lat_1=80.,lat_2=55,lat_0=80,lon_0=-107.)
x,y = m(longitudes,latitudes)

fig = plt.figure(figsize=(12,10))
plt.title("All affected areas")
m.plot(x, y, "o", markersize = 2, color = 'blue')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
m.drawmapboundary()
m.drawcountries()
plt.show()

#train and test data
X = final_data[['Timestamp', 'Latitude', 'Longitude']]
y = final_data[['Magnitude', 'Depth']]
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, X_test.shape)


from keras.models import Sequential
from keras.layers import Dense

def create_model(neurons, activation, optimizer, loss):
    model = Sequential()
    model.add(Dense(neurons, activation=activation, input_shape=(3,)))
    model.add(Dense(neurons, activation=activation))
    model.add(Dense(2, activation='softmax'))
    
    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
    
    return model

#find best parameters

model = KerasClassifier(build_fn=create_model, verbose=0)

#neurons = [16, 64, 128, 256]
neurons = [16]
#batch_size = [10, 20, 50, 100]
batch_size = [10]
epochs = [10]
# activation = ['relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear', 'exponential']
activation = ['sigmoid', 'relu']
# optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
optimizer = ['SGD', 'Adadelta']
loss = ['squared_hinge']

param_grid = dict(neurons=neurons, batch_size=batch_size, epochs=epochs, activation=activation, optimizer=optimizer, loss=loss)

#GridSearchCV
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)

#convert from Numpy array to tensor
X_train = X_train.values.astype('float32')
y_train = y_train.values.astype('float32')

X_test = X_test.values.astype('float32')
y_test = y_test.values.astype('float32')


grid_result = grid.fit(X_train, y_train)


#sequential model
model = Sequential()
model.add(Dense(16, activation='relu', input_shape=(3,)))
model.add(Dense(16, activation='relu'))
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='SGD', loss='squared_hinge', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=10, verbose=1)    

#test
score = model.evaluate(X_test, y_test, batch_size=10)
print(score)

FACCI SAPERE SE FUNZIONA :)

Funziona, funziona, funziona. Quasi riscritto!!!! Grazie e in gamba!

Ho dovuto levare /content/. nella parte read data...

data = pd.read_csv("Dataset_Terremoti.csv")